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Car-focused navigation services are based on turns and distances of namedstreets, whereas navigation instructions naturally used by humans are centeredaround physical objects called landmarks. We present a neural model that takesOpenStreetMap representations as input and learns to generate navigationinstructions that contain visible and salient landmarks from human naturallanguage instructions. Routes on the map are encoded in a location- androtation-invariant graph representation that is decoded into natural languageinstructions. Our work is based on a novel dataset of 7,672 crowd-sourcedinstances that have been verified by human navigation in Street View. Ourevaluation shows that the navigation instructions generated by our system havesimilar properties as human-generated instructions, and lead to successfulhuman navigation in Street View.

Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem